/
posterior_interval.R
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/
posterior_interval.R
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#' Extract posterior intervals for parameters of the model
#'
#' @param object A rater `mcmc_fit` object.
#' @param prob A single probability. The size of the credible interval
#' returned. By default `0.9`.
#' @param pars The parameters to calculate the intervals for
#' @param ... Other arguments.
#'
#' @return A matrix with 2 columns. The first column is the lower bound of
#' of the credible interval and the second is the upper bound. Each row
#' corresponds to one individuals parameters. The rownames are the parameter
#' names.
#'
#' @details Posterior intervals can only be calculated for models fit with
#' MCMC. In addition, posterior intervals are not meaningful for the latent
#' class (and indeed cannot be calculated). The *full* posterior distribution
#' of the latent class can be extracted using [class_probabilities]
#'
#' For the class conditional model the 'full' theta parameterisation (i.e.
#' appearing to have the same number of parameters as the standard
#' Dawid-Skene model) is calculated and returned. This is designed to allow
#' easier comparison with the full Dawid-Skene model.
#'
#' @examples
#'
#' \donttest{
#' fit <- rater(anesthesia, "dawid_skene", verbose = FALSE, chains = 1)
#'
#' intervals <- posterior_interval(fit)
#' head(intervals)
#'
#' }
#'
#' @aliases posterior_interval
#' @method posterior_interval mcmc_fit
#' @importFrom rstantools posterior_interval
#' @export
#' @export posterior_interval
#'
posterior_interval.mcmc_fit <- function(object,
prob = 0.9,
pars = c("pi", "theta"),
...) {
fit <- object
# We could keep the stan data after fitting, but it doesn't seem worth
# the added complexity.
K <- fit$stan_data$K
J <- fit$stan_data$J
intervals <- list()
for (i in 1:length(pars)) {
par <- match.arg(pars[[i]], c("pi", "theta", "z"))
if (par == "pi") {
pi_draws <- posterior_samples(fit, pars = "pi")[[1]]
colnames(pi_draws) <- sprintf("pi[%s]", 1:K)
pi_interval <- rstantools::posterior_interval(pi_draws, prob, ...)
intervals[[i]] <- pi_interval
} else if (par == "theta") {
theta_draws_raw <- posterior_samples(fit, pars = "theta")[[1]]
n_draws <- dim(theta_draws_raw)[[1]]
theta_draws_mat <- matrix(0, nrow = n_draws, ncol = J * K * K)
col_names <- character(J * K * K)
n <- 1
for (j in 1:J) {
for (k in 1:K) {
for (i in 1:K) {
theta_draws_mat[, n] <- theta_draws_raw[, j, k, i]
col_names[[n]] <- sprintf("theta[%s, %s, %s]", j, k, i)
n <- n + 1
}
}
}
colnames(theta_draws_mat) <- col_names
intervals[[i]] <- rstantools::posterior_interval(theta_draws_mat,
prob, ...)
} else if (par == "z") {
stop("Cannot calculate quantiles for z", call. = FALSE)
}
}
do.call(rbind, intervals)
}
#' Extract posterior intervals for parameters of the model
#'
#' @param object A rater optim_fit object
#' @param prob A probability
#' @param pars The parameters to calculate the intervals for
#' @param ... Other arguments
#'
#' @method posterior_interval optim_fit
#' @importFrom rstantools posterior_interval
#' @export
#' @export posterior_interval
#'
posterior_interval.optim_fit <- function(object,
prob = 0.9,
pars = c("pi", "theta"),
...) {
stop("Can't calculate posterior intervals for a model fit using",
" optimisation.",
call. = FALSE)
}